ArticlePloS one2013
Controllability in cancer metabolic networks according to drug targets as driver nodes.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 65 citations in OpenAlex.
- A graph theoretical approach to experimental prioritization in genome-scale investigations.Mammalian genome : official journal of the International Mammalian Genome Society · 2024Article
- Target controllability: a feed-forward greedy algorithm in complex networks, meeting Kalman's rank condition.Bioinformatics (Oxford, England) · 2024Article
- Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets.International journal of molecular sciences · 2023Review
- Total network controllability analysis discovers explainable drugs for Covid-19 treatment.Biology direct · 2023Article
- Cancer-keeper genes as therapeutic targets.iScience · 2023Article
- Total network controllability analysis discovers explainable drugs for Covid-19 treatment.Research square · 2023Article
- Statistical control of structural networks with limited interventions to minimize cellular phenotypic diversity represented by point attractors.Scientific reports · 2023Article
- Metabolic Pathway Analysis: Advantages and Pitfalls for the Functional Interpretation of Metabolomics and Lipidomics Data.Biomolecules · 2023Article
- Characterization of the Gut Microbiota in Urban Thai Individuals Reveals Enterotype-Specific Signature.Microorganisms · 2023Article
- A mechanistic modeling framework reveals the key principles underlying tumor metabolism.PLoS computational biology · 2022Article
- Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer.Frontiers in oncology · 2022Review
- Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer.Frontiers in oncology · 2022Review
- Nodes with the highest control power play an important role at the final level of cooperation in directed networks.Scientific reports · 2021Article
- Opportunities at the Interface of Network Science and Metabolic Modeling.Frontiers in bioengineering and biotechnology · 2020Review
- A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?PloS one · 2020Article
- Identification of critical connectors in the directed reaction-centric graphs of microbial metabolic networks.BMC bioinformatics · 2019Article
- Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology.Journal of clinical medicine · 2019Article
- Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems.Scientific reports · 2019Article
- Metabolic Network-Based Identification and Prioritization of Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma.Frontiers in physiology · 2018Article
- Input graph: the hidden geometry in controlling complex networks.Scientific reports · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.